The IMPROVE Project

IMPROVE will support human’s daily work in distributed production plants whose complexity often exceeds cognitive capabilities. Elaborate tasks will be suitably shifted from human cognitive resources to user decision support systems.

Cognitive tasks of the decision support system rely on modelling of the plant. In order to analyse and optimize a plant’s settings, knowledge about the plant and its production goals must first be captured and adapted to the models. Unlike traditional approaches, all required models are learned by data acquired during operation. As a result, a virtual representation of the real plant, the virtual Factory of the Future (vFoF) is created and maintained during a plant’s complete life-cycle. The learned model is then combined with further knowledge from the engineering phase and subsequently utilised for simulation, optimisation, condition monitoring and diagnosis.

Every step of the IMPROVE research strategy is qualified and implemented by a minimum of two experienced consortium partners, who will conclude the results of the project using four demonstrators. The basis for IMPROVE are actual industrial use-cases, which are transferable to various industrial sectors. The IMPROVE team undertakes the challenges to reduce ramp-up phases, optimize production plants to increase cost-efficiency, reduce time to production through condition monitoring techniques and optimise supply chain holistic data. Consequently, resource consumption, especially energy consumption utilized in manufacturing activities, is targeted for reduction. These enhancements IMPROVE manufacturing during various phases of production, strengthening industrial competitiveness through sustainability in the EU.


  • Create a user support system to interface with machine complexity
  • Create a virtual factory as counterpart to the physical factory
  • Reduce manual modeling effort through innovative data derived model learning techniques
  • Use the virtual factory for simulation, optimization, condition monitoring and diagnosis
  • Develop a decision support system guide and involve users
  • Inspect sociotechnical arrangements to ensure users acceptance
  • Implement and validate using demonstrators in production environments